code: def test_from_save_pretrained(self):
      kwargs = dict(self.forward_default_kwargs)
      num_inference_steps = kwargs.pop("num_inference_steps", None)
  
      for scheduler_class in self.scheduler_classes:
          timestep = self.default_valid_timestep
  
          scheduler_config = self.get_scheduler_config()
          scheduler = scheduler_class(**scheduler_config)
  
          sample = self.dummy_sample
          residual = 0.1 * sample
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(tmpdirname)
              new_scheduler = scheduler_class.from_pretrained(
                  tmpdirname
              )
  
          scheduler.set_timesteps(num_inference_steps)
          new_scheduler.set_timesteps(num_inference_steps)
  
          kwargs["generator"] = torch.manual_seed(0)
          output = scheduler.step(
              residual, timestep, sample, **kwargs
          ).prev_sample
  
          kwargs["generator"] = torch.manual_seed(0)
          new_output = new_scheduler.step(
              residual, timestep, sample, **kwargs
          ).prev_sample
  
          assert (
              torch.sum(torch.abs(output - new_output)) < 1e-5
          ), "Scheduler outputs are not identical"
  
imports: import tempfile
  from typing import Dict, List, Tuple
  import torch
  from diffusers import LCMScheduler
  from diffusers.utils.testing_utils import torch_device
  from .test_schedulers import SchedulerCommonTest
label_desc: Use image node_id 12 for calling the LCMSchedulerTest obj's underlying member method code with example usage: obj.test_from_save_pretrained() without return types